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        <title>VIPHY: Probing “Visible” Physical Commonsense Knowledge</title>
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    <abstract>Vision-language models (VLMs) have shown remarkable performance on visual reasoning tasks (e.g. attributes, location). While such tasks measure the requisite knowledge to ground and reason over a given visual instance, they do not, however, measure the ability of VLMs to retain and generalize such knowledge. In this work, we evaluate VLMs’ ability to acquire “visible” physical knowledge – the information that is easily accessible from images of static scenes, particularly along the dimensions of object color, size, and space. We build an automatic pipeline to derive a comprehensive knowledge resource for calibrating and probing these models. Our results indicate a severe gap between model and human performance across all three dimensions. Furthermore, we demonstrate that a caption pretrained LM significantly outperforms VLMs on both size and spatial tasks – highlighting that despite sufficient access to ground language with visual modality, they struggle to retain such knowledge.</abstract>
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%0 Conference Proceedings
%T VIPHY: Probing “Visible” Physical Commonsense Knowledge
%A Singh, Shikhar
%A Qasemi, Ehsan
%A Chen, Muhao
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F singh-etal-2023-viphy
%X Vision-language models (VLMs) have shown remarkable performance on visual reasoning tasks (e.g. attributes, location). While such tasks measure the requisite knowledge to ground and reason over a given visual instance, they do not, however, measure the ability of VLMs to retain and generalize such knowledge. In this work, we evaluate VLMs’ ability to acquire “visible” physical knowledge – the information that is easily accessible from images of static scenes, particularly along the dimensions of object color, size, and space. We build an automatic pipeline to derive a comprehensive knowledge resource for calibrating and probing these models. Our results indicate a severe gap between model and human performance across all three dimensions. Furthermore, we demonstrate that a caption pretrained LM significantly outperforms VLMs on both size and spatial tasks – highlighting that despite sufficient access to ground language with visual modality, they struggle to retain such knowledge.
%R 10.18653/v1/2023.findings-emnlp.473
%U https://aclanthology.org/2023.findings-emnlp.473
%U https://doi.org/10.18653/v1/2023.findings-emnlp.473
%P 7113-7128

Markdown (Informal)

[VIPHY: Probing “Visible” Physical Commonsense Knowledge](https://aclanthology.org/2023.findings-emnlp.473) (Singh et al., Findings 2023)

• VIPHY: Probing “Visible” Physical Commonsense Knowledge (Singh et al., Findings 2023)

ACL

• Shikhar Singh, Ehsan Qasemi, and Muhao Chen. 2023. VIPHY: Probing “Visible” Physical Commonsense Knowledge. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 7113–7128, Singapore. Association for Computational Linguistics.